Identifying Corrosion From Electromagnetic Corrosion Measurements And High-Resolution Circumferential Measurements

2. The method of claim 1, wherein the one or more high-resolution measurements are for each of the one or more tubulars.

3. The method of claim 2, wherein the one or more high-resolution measurements characterize each of the one or more tubulars.

4. The method of claim 3, combining known pipe nominal outer dimensions and thickness with the one or more high-resolution measurements.

5. The method of claim 1, wherein the relationship between the first EM log data and the database is formed using a deep neural network (DNN).

6. The method of claim 5, wherein the DNN forms the mapping function from the relationship between the first EM log data and the database.

7. The method of claim 1, further comprising identifying corrosion on the one or more tubulars using the mapping function.

8. The method of claim 7, further comprising removing or repairing the one or more tubulars that have the corrosion.

9. The method of claim 1, further comprising identifying a thickness of the one or more tubulars using the mapping function.

10. The method of claim 9, further comprising removing or repairing the one or more tubulars that have corrosion identified by the thickness of the one or more tubulars.

12. The method of claim 11, further comprising taking one or more high-resolution measurements with a high-resolution tool.

13. The method of claim 12, wherein the one or more high-resolution measurements characterize each of the one or more tubulars.

14. The method of claim 13, further comprising combining known pipe nominal outer dimensions and thickness with the one or more high-resolution measurements.

15. The method of claim 13, wherein the high-resolution tool is a flux leakage tool, an ultrasonic tool, or a mechanical caliper tool.

16. The method of claim 13, further comprising creating a resolution-enhanced corrosion map that includes one or more tubular properties using the mapping function, wherein the one or more tubular properties includes a thickness.

17. The method of claim 16, further comprising identifying corrosion on the one or more tubulars using the resolution-enhanced corrosion map.

18. The method of claim 16, further comprising predicting an azimuthal distribution of an anomaly using the resolution-enhanced corrosion map.

19. The method of claim 11, further comprising identifying a thickness of the one or more tubulars using the mapping function and removing or repairing the one or more tubulars that have corrosion identified by the thickness of the one or more tubulars.

20. The method of claim 11, further comprising predicting a thickness of the one or more tubulars using a second set of one or more electromagnetic measurements from the EM logging tool.